def __import_time_series_csv_datatype(self, hrf_folder, connectivity_gid, patient, user_tag): path = os.path.join(hrf_folder, self.TIME_SERIES_CSV_FILE) with open(path) as csv_file: csv_reader = csv.reader( csv_file, delimiter=CSVDelimiterOptionsEnum.COMMA.value) ts = list(csv_reader) ts_data = np.array(ts, dtype=np.float64).reshape( (len(ts), 1, len(ts[0]), 1)) ts_time = np.random.rand(ts_data.shape[0], ) project = dao.get_project_by_id(self.current_project_id) ts_gid = uuid.uuid4() h5_path = "TimeSeries_{}.h5".format(ts_gid.hex) operation_folder = self.storage_interface.get_project_folder( project.name, str(self.operation_id)) h5_path = os.path.join(operation_folder, h5_path) conn = h5.load_from_gid(connectivity_gid) ts = TimeSeriesRegion() ts.data = ts_data ts.time = ts_time ts.gid = ts_gid ts.connectivity = conn generic_attributes = GenericAttributes() generic_attributes.user_tag_1 = user_tag generic_attributes.state = DEFAULTDATASTATE_RAW_DATA with TimeSeriesRegionH5(h5_path) as ts_h5: ts_h5.store(ts) ts_h5.nr_dimensions.store(4) ts_h5.subject.store(patient) ts_h5.store_generic_attributes(generic_attributes) ts_index = TimeSeriesIndex() ts_index.gid = ts_gid.hex ts_index.fk_from_operation = self.operation_id ts_index.time_series_type = "TimeSeriesRegion" ts_index.data_length_1d = ts_data.shape[0] ts_index.data_length_2d = ts_data.shape[1] ts_index.data_length_3d = ts_data.shape[2] ts_index.data_length_4d = ts_data.shape[3] ts_index.data_ndim = len(ts_data.shape) ts_index.sample_period_unit = 'ms' ts_index.sample_period = TimeSeries.sample_period.default ts_index.sample_rate = 1024.0 ts_index.subject = patient ts_index.state = DEFAULTDATASTATE_RAW_DATA ts_index.labels_ordering = json.dumps( list(TimeSeries.labels_ordering.default)) ts_index.labels_dimensions = json.dumps( TimeSeries.labels_dimensions.default) ts_index.visible = False # we don't want to show these TimeSeries because they are dummy dao.store_entity(ts_index) return ts_gid
def test_adapter_launch(self): """ Test that the adapters launches and successfully generates a datatype measure entry. """ meta = { DataTypeMetaData.KEY_SUBJECT: "John Doe", DataTypeMetaData.KEY_STATE: "RAW_DATA" } algo_group = FlowService().get_algorithm_by_module_and_class( SIMULATOR_MODULE, SIMULATOR_CLASS)[1] self.operation = model.Operation(self.test_user.id, self.test_project.id, algo_group.id, json.dumps(''), meta=json.dumps(meta), status=model.STATUS_STARTED, method_name=ABCAdapter.LAUNCH_METHOD) self.operation = dao.store_entity(self.operation) storage_path = FilesHelper().get_project_folder( self.test_project, str(self.operation.id)) dummy_input = numpy.arange(1, 10001).reshape(10, 10, 10, 10) dummy_time = numpy.arange(1, 11) # Get connectivity connectivities = FlowService().get_available_datatypes( self.test_project.id, "tvb.datatypes.connectivity.Connectivity")[0] self.assertEqual(2, len(connectivities)) connectivity_gid = connectivities[0][2] dummy_time_series = TimeSeriesRegion() dummy_time_series.storage_path = storage_path dummy_time_series.write_data_slice(dummy_input) dummy_time_series.write_time_slice(dummy_time) dummy_time_series.close_file() dummy_time_series.start_time = 0.0 dummy_time_series.sample_period = 1.0 dummy_time_series.connectivity = connectivity_gid adapter_instance = StoreAdapter([dummy_time_series]) OperationService().initiate_prelaunch(self.operation, adapter_instance, {}) dummy_time_series = dao.get_generic_entity(dummy_time_series.__class__, dummy_time_series.gid, 'gid')[0] ts_metric_adapter = TimeseriesMetricsAdapter() resulted_metric = ts_metric_adapter.launch(dummy_time_series) self.assertTrue(isinstance(resulted_metric, DatatypeMeasure), "Result should be a datatype measure.") self.assertTrue( len(resulted_metric.metrics) >= len( ts_metric_adapter.available_algorithms.keys()), "At least a result should have been generated for every metric.") for metric_value in resulted_metric.metrics.values(): self.assertTrue(isinstance(metric_value, (float, int)))
def test_adapter_launch(self): """ Test that the adapters launches and successfully generates a datatype measure entry. """ meta = {DataTypeMetaData.KEY_SUBJECT: "John Doe", DataTypeMetaData.KEY_STATE: "RAW_DATA"} algo_group = FlowService().get_algorithm_by_module_and_class(SIMULATOR_MODULE, SIMULATOR_CLASS)[1] self.operation = model.Operation(self.test_user.id, self.test_project.id, algo_group.id, json.dumps(''), meta=json.dumps(meta), status=model.STATUS_STARTED, method_name=ABCAdapter.LAUNCH_METHOD) self.operation = dao.store_entity(self.operation) storage_path = FilesHelper().get_project_folder(self.test_project, str(self.operation.id)) dummy_input = numpy.arange(1, 10001).reshape(10, 10, 10, 10) dummy_time = numpy.arange(1, 11) # Get connectivity connectivities = FlowService().get_available_datatypes(self.test_project.id, "tvb.datatypes.connectivity.Connectivity")[0] self.assertEqual(2, len(connectivities)) connectivity_gid = connectivities[0][2] dummy_time_series = TimeSeriesRegion() dummy_time_series.storage_path = storage_path dummy_time_series.write_data_slice(dummy_input) dummy_time_series.write_time_slice(dummy_time) dummy_time_series.close_file() dummy_time_series.start_time = 0.0 dummy_time_series.sample_period = 1.0 dummy_time_series.connectivity = connectivity_gid adapter_instance = StoreAdapter([dummy_time_series]) OperationService().initiate_prelaunch(self.operation, adapter_instance, {}) dummy_time_series = dao.get_generic_entity(dummy_time_series.__class__, dummy_time_series.gid, 'gid')[0] ts_metric_adapter = TimeseriesMetricsAdapter() resulted_metric = ts_metric_adapter.launch(dummy_time_series) self.assertTrue(isinstance(resulted_metric, DatatypeMeasure), "Result should be a datatype measure.") self.assertTrue(len(resulted_metric.metrics) >= len(ts_metric_adapter.available_algorithms.keys()), "At least a result should have been generated for every metric.") for metric_value in resulted_metric.metrics.values(): self.assertTrue(isinstance(metric_value, (float, int)))